---
id: pycocoevalcap
version: "1.2"
license: unclear
license_treatment: unclear
maintenance: dormant
---

# pycocoevalcap — MS-COCO Caption Evaluation for Python 3

License: unclear · Maintenance: dormant · Downloads: 1.0M/mo

## What it is and what it does

Pycocoevalcap is a Python 3 port of the original MS COCO caption evaluation toolkit, providing five automatic metrics for assessing the quality of machine-generated image captions. It wraps evaluation algorithms (BLEU, METEOR, ROUGE-L, CIDEr, and SPICE) that compare generated captions to reference captions, producing scores that measure different aspects of caption quality—fluency, semantic similarity, and semantic propositional content.

The package depends on pycocotools for the COCO API and requires Java 1.8.0 for some components. SPICE evaluation automatically downloads Stanford CoreNLP on first use and caches parsed sentences to speed up repeated evaluations. The package is stable but dormant; it has not been updated since its initial release in November 2020, though the repository remains accessible and the underlying evaluation metrics are well-established in the computer vision and NLP research communities.

Use it for:

- Benchmark image captioning models against MS COCO dataset using standardized evaluation metrics.
- Compare multiple caption generation systems with consistent, reproducible scores.
- Validate caption quality during model development and hyperparameter tuning.
- Generate evaluation reports for research papers using established COCO evaluation protocols.
- Integrate caption evaluation into automated testing pipelines for vision-language models.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Evaluates image captions using multiple automatic metrics (BLEU, METEOR, ROUGE-L, CIDEr, SPICE) designed for the MS COCO dataset, providing Python 3 support for caption generation assessment.

Yes, if you are evaluating captions on the MS COCO dataset or need the specific metrics this package provides. The low install friction, stable API, and absence of known vulnerabilities make it reliable for established evaluation workflows. However, verify the license terms first, and be aware that the package is dormant—no updates are expected, and you should confirm that all five metrics work correctly in your environment, particularly SPICE's Java and CoreNLP dependencies.

## Install

pip install pycocoevalcap
uv add pycocoevalcap
poetry add pycocoevalcap

## Installing pycocoevalcap

Before you install: Low install friction with a single runtime dependency (pycocotools). The package is dormant—last release was 2020-11-18 and no updates since, though the repository remains active with recent commits (2024-08-01). Suitable for stable evaluation workflows but not for ongoing feature development.

License in practice: License status is unclear; no SPDX identifier or raw license text is provided. Verify the actual license terms in the repository before using in proprietary or restricted contexts.

Quickstart:

pip install pycocoevalcap

from pycocoevalcap.eval import COCOEvalCap

coco_eval = COCOEvalCap(coco, coco_result)
coco_eval.evaluate()

Java 1.8.0 is required; SPICE metric will automatically download Stanford CoreNLP 3.6.0 on first use.

Verify before relying:

- Whether the package's license is compatible with your intended use (license treatment is unclear).
- Current state of SPICE's Stanford CoreNLP download and cache behavior in modern environments.
- Whether all five metrics (BLEU, METEOR, ROUGE-L, CIDEr, SPICE) are equally maintained and reliable.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 1.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

caption evaluation metrics, COCO caption scoring, image description evaluation, BLEU METEOR ROUGE CIDEr SPICE, MS COCO evaluation, caption quality assessment, automatic caption metrics, caption-evaluation, coco-dataset, nlp-metrics

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